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            "Collecting transformers\n",
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            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
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            "  Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
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            "\u001b[?25hCollecting multiprocess (from datasets->trl)\n",
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            "Installing collected packages: tokenizers, safetensors, xxhash, dill, multiprocess, huggingface-hub, transformers, datasets, accelerate, trl\n",
            "Successfully installed accelerate-0.22.0 datasets-2.14.4 dill-0.3.7 huggingface-hub-0.16.4 multiprocess-0.70.15 safetensors-0.3.3 tokenizers-0.13.3 transformers-4.33.0 trl-0.7.1 xxhash-3.3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "hgj2hw_WQecA"
      },
      "outputs": [],
      "source": [
        "## Import libraries\n",
        "import torch\n",
        "import transformers\n",
        "from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, DataCollatorForLanguageModeling\n",
        "from trl import RewardTrainer, SFTTrainer\n",
        "from datasets import Dataset\n",
        "import json\n",
        "import pandas as pd\n",
        "from transformers import Trainer, TrainingArguments"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://huggingface.co/datasets/CarperAI/openai_summarize_comparisons/resolve/main/data/test-00000-of-00001-0845e2eec675b16a.parquet\n",
        "# after that rename to test.parquet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NFgC7kBFRm0P",
        "outputId": "b23a409d-ff09-4f49-a120-083a0be42d4a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-09-06 12:36:39--  https://huggingface.co/datasets/CarperAI/openai_summarize_comparisons/resolve/main/data/test-00000-of-00001-0845e2eec675b16a.parquet\n",
            "Resolving huggingface.co (huggingface.co)... 18.172.134.24, 18.172.134.4, 18.172.134.88, ...\n",
            "Connecting to huggingface.co (huggingface.co)|18.172.134.24|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://cdn-lfs.huggingface.co/repos/02/84/02842250b699bbde4e980e9206f5045df542f9ca374595cc86b8d57189641ff5/a8c5d73113a2c7119faacd44e43959f74e5b59e181f3da57caa918c907ac6620?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27test-00000-of-00001-0845e2eec675b16a.parquet%3B+filename%3D%22test-00000-of-00001-0845e2eec675b16a.parquet%22%3B&Expires=1694262999&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY5NDI2Mjk5OX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy8wMi84NC8wMjg0MjI1MGI2OTliYmRlNGU5ODBlOTIwNmY1MDQ1ZGY1NDJmOWNhMzc0NTk1Y2M4NmI4ZDU3MTg5NjQxZmY1L2E4YzVkNzMxMTNhMmM3MTE5ZmFhY2Q0NGU0Mzk1OWY3NGU1YjU5ZTE4MWYzZGE1N2NhYTkxOGM5MDdhYzY2MjA%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=CLl3zBeYW5jzKbX9MZkryL0p-j8lTtx03ksho0Zwpi5ZeKplDz%7EohUhbXaK5OKCteoEJx%7EDjpluciHcFdC%7EQcfdwgRSuAuT%7ECMaPxAmmzR54Zd0AcIPR02OenGpIbf3scib4X-ZCWgSVAgUlF6F8jfbSq1Yo4eoi4iyJj3m%7El1O0NbWVZ7qm%7E4kXfNzBpaMI1l0pDg-9x6UCrVuto2g1guD3Uzwb%7EahiEwCYIcVGLgxo-SXOFO7-9w4eCZ-KkvwNq%7EgEGeUBmilzf1Wwnu6IVPnygYicEbiAIopHZDcm87wuC3j%7EcMwVKQx9JxrfXf3kP-Q6OqrLDUd2ejN0gInvUQ__&Key-Pair-Id=KVTP0A1DKRTAX [following]\n",
            "--2023-09-06 12:36:39--  https://cdn-lfs.huggingface.co/repos/02/84/02842250b699bbde4e980e9206f5045df542f9ca374595cc86b8d57189641ff5/a8c5d73113a2c7119faacd44e43959f74e5b59e181f3da57caa918c907ac6620?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27test-00000-of-00001-0845e2eec675b16a.parquet%3B+filename%3D%22test-00000-of-00001-0845e2eec675b16a.parquet%22%3B&Expires=1694262999&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY5NDI2Mjk5OX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy8wMi84NC8wMjg0MjI1MGI2OTliYmRlNGU5ODBlOTIwNmY1MDQ1ZGY1NDJmOWNhMzc0NTk1Y2M4NmI4ZDU3MTg5NjQxZmY1L2E4YzVkNzMxMTNhMmM3MTE5ZmFhY2Q0NGU0Mzk1OWY3NGU1YjU5ZTE4MWYzZGE1N2NhYTkxOGM5MDdhYzY2MjA%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=CLl3zBeYW5jzKbX9MZkryL0p-j8lTtx03ksho0Zwpi5ZeKplDz%7EohUhbXaK5OKCteoEJx%7EDjpluciHcFdC%7EQcfdwgRSuAuT%7ECMaPxAmmzR54Zd0AcIPR02OenGpIbf3scib4X-ZCWgSVAgUlF6F8jfbSq1Yo4eoi4iyJj3m%7El1O0NbWVZ7qm%7E4kXfNzBpaMI1l0pDg-9x6UCrVuto2g1guD3Uzwb%7EahiEwCYIcVGLgxo-SXOFO7-9w4eCZ-KkvwNq%7EgEGeUBmilzf1Wwnu6IVPnygYicEbiAIopHZDcm87wuC3j%7EcMwVKQx9JxrfXf3kP-Q6OqrLDUd2ejN0gInvUQ__&Key-Pair-Id=KVTP0A1DKRTAX\n",
            "Resolving cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)... 18.154.185.26, 18.154.185.64, 18.154.185.94, ...\n",
            "Connecting to cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)|18.154.185.26|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 20441979 (19M) [binary/octet-stream]\n",
            "Saving to: ‘test-00000-of-00001-0845e2eec675b16a.parquet’\n",
            "\n",
            "test-00000-of-00001 100%[===================>]  19.49M   105MB/s    in 0.2s    \n",
            "\n",
            "2023-09-06 12:36:39 (105 MB/s) - ‘test-00000-of-00001-0845e2eec675b16a.parquet’ saved [20441979/20441979]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset for Training : https://huggingface.co/datasets/CarperAI/openai_summarize_tldr\n",
        "\n",
        "### Dataset for Testing : https://huggingface.co/datasets/CarperAI/openai_summarize_comparisons"
      ],
      "metadata": {
        "id": "r81RnxurR-E2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Model_name = \"bigcode/tiny_starcoder_py\"\n",
        "Data_path = \"test.parquet\""
      ],
      "metadata": {
        "id": "sY4LnQyVQl5J"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Read the file\n",
        "df  = pd.read_parquet(Data_path)\n",
        "df = df[:10]\n",
        "raw_dataset = Dataset.from_pandas(df)\n",
        "raw_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ReMAX6WySL3s",
        "outputId": "7dbda935-3fa6-47fd-a5a9-3ce66a006f3c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['prompt', 'chosen', 'rejected'],\n",
              "    num_rows: 10\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tokenize the dataset to feed in the model"
      ],
      "metadata": {
        "id": "1IPbHllpSeYn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(Model_name)\n",
        "model = AutoModelForCausalLM.from_pretrained(Model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273,
          "referenced_widgets": [
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        "id": "daw09BDBSa44",
        "outputId": "38e185ab-cfa4-457c-cb2c-aa9f4cf01532"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/677 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
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              "version_major": 2,
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          },
          "metadata": {}
        },
        {
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            ],
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tokenize the dataset to feed in the model"
      ],
      "metadata": {
        "id": "Z15PFpEhS51h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
        "def formatting_func(examples):\n",
        "    kwargs = {\"padding\": \"max_length\",\n",
        "              \"truncation\": True,\n",
        "              \"max_length\": 256,\n",
        "              \"return_tensors\": \"pt\"\n",
        "              }\n",
        "\n",
        "    # Prepend the prompt and a line break to the original_response and response-1 fields.\n",
        "    prompt_plus_chosen_response = examples[\"prompt\"] + \"\\n\" + examples[\"chosen\"]\n",
        "    prompt_plus_rejected_response = examples[\"prompt\"] + \"\\n\" + examples[\"rejected\"]\n",
        "\n",
        "    # Then tokenize these modified fields.\n",
        "    tokens_chosen = tokenizer.encode_plus(prompt_plus_chosen_response, **kwargs)\n",
        "    tokens_rejected = tokenizer.encode_plus(prompt_plus_rejected_response, **kwargs)\n",
        "\n",
        "    return {\n",
        "        \"input_ids_chosen\": tokens_chosen[\"input_ids\"][0], \"attention_mask_chosen\": tokens_chosen[\"attention_mask\"][0],\n",
        "        \"input_ids_rejected\": tokens_rejected[\"input_ids\"][0], \"attention_mask_rejected\": tokens_rejected[\"attention_mask\"][0]\n",
        "    }"
      ],
      "metadata": {
        "id": "gcAtMRzpS2aC"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Map the function to data"
      ],
      "metadata": {
        "id": "qec70IWWVYkU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "formatted_dataset = raw_dataset.map(formatting_func)\n",
        "formatted_dataset = formatted_dataset.train_test_split()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "6396b18205ab4daa965b76a29a8e8fda",
            "df34019ab4154eb38dcce6ed6d204fbc",
            "fff7525af65f4bed8f1ef699603f3c07",
            "733550fcf3154e25ba0ace779ceb87a7",
            "cd99115f95bb4282910caf8e4f03612e",
            "4561689ef64b40d2a41ccf58815959bd",
            "0a3eb086e97f459bb3a032ac9ec22dea",
            "9fce344d959f46a6b28c2f8726c704b4",
            "92085ca3991f46b5b626f00d665788ed",
            "be943a11e101479dbbf8f176f3783cd6",
            "6c6448ae3f764b32b99b2a0e21d85184"
          ]
        },
        "id": "b5q-aIpRUCxP",
        "outputId": "4578f0f4-09c3-45ca-f164-d1ace2c2525d"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/10 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6396b18205ab4daa965b76a29a8e8fda"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.config"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ejl2Q_iVXtn",
        "outputId": "b888e6bc-7fea-4515-bb67-1dc9367fe9cf"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPTBigCodeConfig {\n",
              "  \"_name_or_path\": \"bigcode/tiny_starcoder_py\",\n",
              "  \"activation_function\": \"gelu_pytorch_tanh\",\n",
              "  \"architectures\": [\n",
              "    \"GPTBigCodeForCausalLM\"\n",
              "  ],\n",
              "  \"attention_softmax_in_fp32\": true,\n",
              "  \"attn_pdrop\": 0.1,\n",
              "  \"bos_token_id\": 0,\n",
              "  \"embd_pdrop\": 0.1,\n",
              "  \"eos_token_id\": 0,\n",
              "  \"inference_runner\": 0,\n",
              "  \"initializer_range\": 0.02,\n",
              "  \"layer_norm_epsilon\": 1e-05,\n",
              "  \"max_batch_size\": null,\n",
              "  \"max_sequence_length\": null,\n",
              "  \"model_type\": \"gpt_bigcode\",\n",
              "  \"multi_query\": true,\n",
              "  \"n_embd\": 768,\n",
              "  \"n_head\": 12,\n",
              "  \"n_inner\": 3072,\n",
              "  \"n_layer\": 20,\n",
              "  \"n_positions\": 8192,\n",
              "  \"pad_key_length\": true,\n",
              "  \"pre_allocate_kv_cache\": false,\n",
              "  \"resid_pdrop\": 0.1,\n",
              "  \"scale_attention_softmax_in_fp32\": true,\n",
              "  \"scale_attn_weights\": true,\n",
              "  \"summary_activation\": null,\n",
              "  \"summary_first_dropout\": 0.1,\n",
              "  \"summary_proj_to_labels\": true,\n",
              "  \"summary_type\": \"cls_index\",\n",
              "  \"summary_use_proj\": true,\n",
              "  \"torch_dtype\": \"float32\",\n",
              "  \"transformers_version\": \"4.33.0\",\n",
              "  \"use_cache\": true,\n",
              "  \"validate_runner_input\": true,\n",
              "  \"vocab_size\": 49152\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Loading the TRL reward trainer and training the trainer\n",
        "training_args = TrainingArguments(\n",
        "        output_dir=\"tinystarcoder-rlhf-model\",\n",
        "        num_train_epochs=1,\n",
        "        logging_steps=10,\n",
        "        gradient_accumulation_steps=1,\n",
        "        save_strategy=\"steps\",\n",
        "        evaluation_strategy=\"steps\",\n",
        "        per_device_train_batch_size=2,\n",
        "        per_device_eval_batch_size=1,\n",
        "        eval_accumulation_steps=1,\n",
        "        eval_steps=500,\n",
        "        save_steps=500,\n",
        "        warmup_steps=100,\n",
        "        logging_dir=\"./logs\",\n",
        "        learning_rate=1e-5,\n",
        "        save_total_limit=1,\n",
        "        no_cuda=True\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S4UivLiGVgO2",
        "outputId": "cb75c51d-59b6-4fcf-841d-e0b5c62ee074"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1257: FutureWarning: using `no_cuda` is deprecated and will be removed in version 5.0 of 🤗 Transformers. Use `use_cpu` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = RewardTrainer(model=model,\n",
        "                        tokenizer=tokenizer,\n",
        "                        train_dataset=formatted_dataset['train'],\n",
        "                        eval_dataset=formatted_dataset['test'],\n",
        "                        args= training_args\n",
        "                        )\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "P2ooNlBhVim-",
        "outputId": "259a4a95-1b91-4c2b-cb70-23b64cc267ca"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/reward_trainer.py:124: UserWarning: When using RewardDataCollatorWithPadding, you should set `max_length` in the RewardTrainer's init it will be set to `512` by default, but you should do it yourself in the future.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/reward_trainer.py:138: UserWarning: When using RewardDataCollatorWithPadding, you should set `remove_unused_columns=False` in your TrainingArguments we have set it for you, but you should do it yourself in the future.\n",
            "  warnings.warn(\n",
            "You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2436: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  warnings.warn(\n",
            "Could not estimate the number of tokens of the input, floating-point operations will not be computed\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='4' max='4' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [4/4 00:52, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=4, training_loss=0.7674217224121094, metrics={'train_runtime': 101.4459, 'train_samples_per_second': 0.069, 'train_steps_per_second': 0.039, 'total_flos': 0.0, 'train_loss': 0.7674217224121094, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "id": "ENADz21Gf1XN",
        "outputId": "eecf2422-7dda-498a-c2d8-479f3aab6b72"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2436: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/reward_trainer.py:214: UserWarning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matrices or `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3571.)\n",
            "  logits = torch.stack(logits).mean(dim=2).softmax(dim=0).T\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3' max='3' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3/3 00:14]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.6931466460227966,\n",
              " 'eval_accuracy': 0.0,\n",
              " 'eval_runtime': 22.5484,\n",
              " 'eval_samples_per_second': 0.133,\n",
              " 'eval_steps_per_second': 0.133,\n",
              " 'epoch': 1.0}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import login\n",
        "login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "70363dc337a84a4e8d9dbe548bb24e5a",
            "b50c4b7e474f4b3ca1b626b8390f6d4e",
            "5b58c1033da8470f82744583306674e4",
            "85c7e1bc8dcb4ee99baeb9f30cfdd2bb",
            "2d293c8541264b0cbaeb7219c2d94ef8",
            "a634b0ccd0a84f53acbb16ede00688b7",
            "2f2cd4fd194444aeb41ab473ce74f744",
            "cbcda32fd6694581a3ea49c2abc83232",
            "f3b324fe716e4abc81fcdd0817387797",
            "9fbdfbeef5ed4880ae68fcde7d12b617",
            "b7653907cb634671b17cde0234bdaf18",
            "9fd5e7396838452da35d5f1240dcfc09",
            "b6fe4d9105d849da8c69966016b52454",
            "37b3bc877baa429e99456abc6e47e551",
            "61e3ec46b6ca4bfaa5cffe684130d13b",
            "1b1bb860813d45bb88f39b1aaa192e91",
            "f8a983b16f3644fca1692d8de9f07f0d",
            "89cc8bd26d154bbd812997f60b0e241d",
            "6511db857f284380a6d93b9115d14479",
            "f387c978d4c24221890d90ac8d22e0b6",
            "79131c98086a4259800d77bd94f81554",
            "b015f478b8b6453fa0f49c115ff572c2",
            "2d9d192cfa124aadaf1b3d31bb9dd0ce",
            "cb52479c2c3844a99388a8e71fd6d0e6",
            "9d2d72d38d5f43fcbd64b43481982ce8",
            "7218cccb90284684b43124ee11adb918",
            "1b93ff678ada4ff5bec69a1bdb36bd5b",
            "1d5e9cd1f7234b899efbfd3cfb23774c",
            "a6295ffea3b546e9a9b62552424dc970",
            "554b5751193b4133992e160b6a938dda",
            "9edaf08284f24cd6bd37039f565315f2",
            "1ff9cb3284ad4ffd9eb7ca376e5e3006"
          ]
        },
        "id": "ZI4Ex-o7WFHc",
        "outputId": "4ca5f4f0-44ac-46be-8976-8697f79018cc"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70363dc337a84a4e8d9dbe548bb24e5a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(\"RLHF model of StarCoder\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "7511afb8ae6b4947a2445ef0450d449f",
            "df5badd1ab29425d8ed70a91d618a0e4",
            "c89e2bd3496a46ed83c1678b2f9d1713",
            "2fa045000acd438da96717b13d3c5a0c",
            "a8434eed07314c24935c6ca6a9c4952e",
            "74551940aad54d309c95ce1d549438e0",
            "4fe71e691bc04d609baeb07bfe0bd4e1",
            "cce70368ebaf4d2a83f4bd8fc87b827f",
            "7b6da5b0aa0744bba4041954252279fa",
            "e9a71eebb4084b6fb7b2efd8bfc8fa14",
            "312bca73ede940f280ea3c25206ef3f8"
          ]
        },
        "id": "bUrhJDQLWDuu",
        "outputId": "bb69e358-4c0a-4a0d-9cbc-8bdb35017c9a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/657M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7511afb8ae6b4947a2445ef0450d449f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/ashishpatel26/tinystarcoder-rlhf-model/tree/main/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_model(\"tinystarcoder-rlhf-model\")"
      ],
      "metadata": {
        "id": "jb3_r6pcVqDx"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Policy Model"
      ],
      "metadata": {
        "id": "pvXbToUJV7Y9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "help(trainer.push_to_hub)"
      ],
      "metadata": {
        "id": "8dfAGf2Ha20p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## inference the model\n",
        "rm_model = AutoModelForCausalLM.from_pretrained(\"tinystarcoder-rlhf-model\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"tinystarcoder-rlhf-model\")"
      ],
      "metadata": {
        "id": "E_JNgUoqV8cL"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_score(model, tokenizer, prompt, response):\n",
        "\n",
        "    instructions = tokenizer.encode_plus(prompt,\n",
        "                                       response,\n",
        "                                       padding=\"max_length\",\n",
        "                                       max_length=256,\n",
        "                                       return_tensors=\"pt\",\n",
        "                                        truncation=True)\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**instructions)\n",
        "\n",
        "    logits = outputs[0]\n",
        "\n",
        "    return logits"
      ],
      "metadata": {
        "id": "YyFiSSkkZUSt"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# usage with prompt\n",
        "prompt = df.iloc[0][\"prompt\"]\n",
        "example_prefered_response = df.iloc[0][\"chosen\"]\n",
        "example_unprefered_response = df.iloc[0][\"rejected\"]"
      ],
      "metadata": {
        "id": "BUNv_muAZcrB"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss1 = get_score(model, tokenizer, prompt, example_prefered_response)\n",
        "loss2= get_score(model, tokenizer, prompt, example_unprefered_response)"
      ],
      "metadata": {
        "id": "uV6LkWkWZePB"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import nn\n",
        "loss = -nn.functional.logsigmoid(loss1 - loss2).mean()"
      ],
      "metadata": {
        "id": "0E6NWxhPZgVB"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.decode(torch.max(loss1, axis=-1).indices[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "id": "IhbtDog6Zh0s",
        "outputId": "74f7678a-6edc-48ba-804f-16cd63eb4af9"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'_DDIT_\\n           \"r/#: Relationship RelationshipRelationship0]0ARCH\\nlsriend\\n2//M]\\n [\\n [\\n the was to the [\\n a friends to\\n\\n: https [lfriend [ me am a aumb. my19 minutes.\\n\\n\"\"\"I updated:** girlfriend was through my Facebook..** I my my friends.**.** my  of dayslf.\\n\\n** went dding for my message personirl** I had to makeellpping my my my past** but I was a in\\n\\n** have ali a of ago she  tolirt with my girl. and she had my about my.. me few of gir.1viously). was\\'t find that was).\\n\\n** was it for  twoirl and the had my Facebook. the   she wasand historyirl) was was March,\\n to to find that were flited. I a g.. f.ing on her.\\nirirl wass; I1 girirllfriend is 19 months. through my Facebook.. my permission. I it messages. her.lirty with me coupleirl.\\n found up with me after she through more with\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Policy Model"
      ],
      "metadata": {
        "id": "yIjodhd5hP7g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import transformers\n",
        "from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, DataCollatorForLanguageModeling\n",
        "from trl import RewardTrainer, SFTTrainer\n",
        "from datasets import Dataset\n",
        "import json\n",
        "import pandas as pd\n",
        "from transformers import Trainer, TrainingArguments\n",
        "from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead, create_reference_model"
      ],
      "metadata": {
        "id": "m_7o8be1ZpfJ"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##model path\n",
        "MODEL_PATH = \"bigcode/tiny_starcoder_py\"\n",
        "DATA_PATH = \"test.parquet\""
      ],
      "metadata": {
        "id": "7fZZYLbrhRom"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_parquet(DATA_PATH)\n",
        "df = df[:1000]\n",
        "dataset = Dataset.from_pandas(df)\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6vJxLQvYhU23",
        "outputId": "85339387-e6f3-441c-f3f8-5322e87005fe"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['prompt', 'chosen', 'rejected'],\n",
              "    num_rows: 1000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, pipeline"
      ],
      "metadata": {
        "id": "o74znYVghvy5"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model_path = \"tinystarcoder-rlhf-model\"\n",
        "starcoder_model = AutoModelForCausalLMWithValueHead.from_pretrained(rf_model_path)\n",
        "starcoder_model_ref = AutoModelForCausalLMWithValueHead.from_pretrained(rf_model_path)\n",
        "starcoder_tokenizer = AutoTokenizer.from_pretrained(rf_model_path)\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, padding_side='left')\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ],
      "metadata": {
        "id": "NCrrq1WEjWYm"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "txt_in_len = 5\n",
        "txt_out_len = 32\n",
        "seed = 1\n",
        "\n",
        "dataset = dataset.map(\n",
        "    lambda x: {\"input_ids\": tokenizer.encode(\" \" + x[\"chosen\"], return_tensors=\"pt\", truncation=True, padding=\"max_length\", max_length=32)[0]},\n",
        "    batched=False,\n",
        ")\n",
        "dataset = dataset.map(lambda x: {\"query\": tokenizer.decode(x[\"input_ids\"])}, batched=False)\n",
        "dataset = dataset[:20480]\n",
        "from datasets import Dataset\n",
        "\n",
        "dataset = Dataset.from_dict(dataset)\n",
        "dataset.set_format(\"pytorch\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "1a97f217b8974c71979590b3921142f4",
            "8cf22559c709440da8afa8284f5721c8",
            "e7e2120d9a6d46cc9198d3ec6227d731",
            "f51b130d5bee4a3f9856d786feeb49dd",
            "4ec54d6d87b54e589350489b434035fe",
            "f49f4dc7622a421f8b51e5dbf42b9897",
            "c34cf7b8aae94de9bbae518c124f11be",
            "6cad2953fe054449b1c3c57dc12cb2da",
            "63b5f2cc7f4e4dd889e3e0ce1218abb3",
            "117f42f3c9664da481bf04bbbc3b5021",
            "a922b8c9424b4576824246bff7023f89",
            "d60942cf3d974c71bd1f9d5984e0580c",
            "d4d7add22a154279943428d496ac05ca",
            "42d9369bf5bc4f538f925e0fd0e3048f",
            "bbe71558dbf0434a9334aba784e547ac",
            "5d677887f4074e288eb95c7dba354680",
            "aa6374e9313348bda63dfd8bad97481a",
            "c8c318424b2b4ba0b788d7a15217ddbe",
            "8614f95764c0409da8dd82ec9ad474f1",
            "9820beb8b5d44759b81e6ca97ff7b250",
            "07b8e67462694703a1d9dbed9f9d5c1b",
            "88a6e00a597d4e7394cdd42386d987a7"
          ]
        },
        "id": "WDPj8gqqjdBY",
        "outputId": "a405d2a3-6a76-4781-cc0d-1ca70a4dd688"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1a97f217b8974c71979590b3921142f4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d60942cf3d974c71bd1f9d5984e0580c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jkQnoXkwj96E",
        "outputId": "136d7e64-9d45-4552-97d9-31cbe6a40e85"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['review', 'chosen', 'rejected', 'input_ids', 'query'],\n",
              "    num_rows: 1000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sentiment_pipe_kwargs = {\"top_k\": None, \"function_to_apply\": \"none\"}\n",
        "\n",
        "config = PPOConfig(\n",
        "    model_name=MODEL_PATH, steps=51200, learning_rate=1.41e-5, remove_unused_columns=True\n",
        ")\n",
        "\n",
        "txt_in_len = 5\n",
        "txt_out_len = 20\n",
        "seed = 1"
      ],
      "metadata": {
        "id": "f9F3y_XDkAvj"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "optimizer = torch.optim.SGD(starcoder_model.parameters(), lr=config.learning_rate)\n",
        "ppo_trainer = PPOTrainer(config, starcoder_model, starcoder_model, starcoder_tokenizer, dataset=dataset, data_collator=collator, optimizer=optimizer)"
      ],
      "metadata": {
        "id": "bJYnA8hZkTEK"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# for i in ppo_trainer.dataloader:\n",
        "#   print(i)"
      ],
      "metadata": {
        "id": "i_RWvbQDkgsx"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ctrl_str = [\"[negative]\", \"[positive]\"]\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")  # this should be handled by accelerate\n",
        "ctrl_tokens = dict((s, starcoder_tokenizer.encode(s, return_tensors=\"pt\").squeeze().to(device)) for s in ctrl_str)"
      ],
      "metadata": {
        "id": "YFs55MmokaWm"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def pos_logit_to_reward(logit, task):\n",
        "    \"\"\"\n",
        "    Take the positive sentiment logit and scale it for the task.\n",
        "        task [negative]: reward = -logit\n",
        "        task [neutral]: reward = -2*abs(logit)+4\n",
        "        task [positive]: reward = logit\n",
        "    \"\"\"\n",
        "    for i in range(len(logit)):\n",
        "        if task[i] == \"[negative]\":\n",
        "            logit[i] = -logit[i]\n",
        "        elif task[i] == \"[positive]\":\n",
        "            pass\n",
        "        else:\n",
        "            raise ValueError(\"task has to be in [0, 1, 2]!\")\n",
        "    return logit"
      ],
      "metadata": {
        "id": "g6EMveqTkuNW"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pos_logit_to_reward(torch.Tensor([4, 4]), ctrl_str)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7INpXsiskue5",
        "outputId": "fc0be95d-5b3a-4bb2-8b29-45bb91f66016"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([-4.,  4.])"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "generation_kwargs = {\n",
        "    \"min_length\": -1,\n",
        "    \"top_k\": 0.0,\n",
        "    \"top_p\": 1.0,\n",
        "    \"do_sample\": True,\n",
        "    \"pad_token_id\": starcoder_tokenizer.eos_token_id,\n",
        "    \"max_new_tokens\": 32,\n",
        "    \"eos_token_id\": -1,\n",
        "}"
      ],
      "metadata": {
        "id": "Y-oelQ_qkv0D"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_score(model, tokenizer, responses):\n",
        "    positive_logist = []\n",
        "    for i in responses:\n",
        "        instructions = tokenizer.encode_plus(\n",
        "                                           i,\n",
        "                                           padding=\"max_length\",\n",
        "                                           max_length=32,\n",
        "                                           return_tensors=\"pt\")\n",
        "        with torch.no_grad():\n",
        "            outputs = model(**instructions)\n",
        "\n",
        "        logits = outputs[0].mean()\n",
        "        positive_logist.append(logits)\n",
        "\n",
        "    return positive_logist\n",
        ""
      ],
      "metadata": {
        "id": "hs9DCR5Lk0Ut"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# responses =[\"ashish is a goo\", \"heelow how are you\", \"__IT_\\nr/\\n: r RelationshipRelationship]]0]\\nlsriend\\n2//M]\\n [ [ a\\n the was to the [. a friends to\\n\\n:\\n [lfriend [ me have a aried in his19 minutes.\\n\\nWhat Modified:** girlfriend was through the Facebook.. I my my friends.**** my  of lf**\\n\\n** was d1ing for my few personirl** I had for findoolpping my my the future** but I was that in\\n\\n** have ali  of to she  tolirt my me girl. and she found my about my.. me few of gir.1viously). was\\'t find her was).\\n\\n** was it about my twoirl and the had  Facebook. the  and she gand historyirl) was in April,\\n to, find, were flirted. I a messages.. f.ing on her.\\n girlM\\n; I1 girirllfriend and the19 months. to my Facebook.. my permission. she her messages. my.lirty with my fewirl.\\n found her with me. I through more with\\n\"]\n",
        "# get_score(starcoder_model, tokenizer, responses)"
      ],
      "metadata": {
        "id": "PV0W3vzzlAK7"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from random import choices\n",
        "# from tqdm import tqdm\n",
        "# import time\n",
        "# import numpy as np\n",
        "\n",
        "# for epoch in range(1):\n",
        "#     for batch in tqdm(ppo_trainer.dataloader):\n",
        "#         (logs, game_data,) = (\n",
        "#             dict(),\n",
        "#             dict(),\n",
        "#         )\n",
        "\n",
        "#         print(ctrl_str)\n",
        "#         #### prepend a random control token\n",
        "#         task_list = choices(ctrl_str, k=config.batch_size)\n",
        "#         game_data[\"query\"] = [t + q for t, q in zip(task_list, batch[\"query\"])]\n",
        "#         query_tensors = [torch.cat((ctrl_tokens[t], input_ids)) for t, input_ids in zip(task_list, batch[\"input_ids\"])]\n",
        "\n",
        "#         #### get response from gpt2\n",
        "#         response_tensors = []\n",
        "#         for query in query_tensors:\n",
        "#             response = ppo_trainer.generate(query, **generation_kwargs)\n",
        "#             response_tensors.append(response.squeeze()[-txt_out_len:])\n",
        "# #         print(response_tensors)\n",
        "#         game_data[\"response\"] = [starcoder_tokenizer.decode(r.squeeze()) for r in response_tensors]\n",
        "\n",
        "#         #### sentiment analysis\n",
        "#         texts = [q + r for q, r in zip(batch[\"query\"], game_data[\"response\"])]\n",
        "#         logits = get_score(starcoder_model,starcoder_tokenizer, texts)\n",
        "#         rewards = pos_logit_to_reward(logits, task_list)\n",
        "\n",
        "#         #### Run PPO training\n",
        "#         t = time.time()\n",
        "#         stats = ppo_trainer.step(query_tensors, response_tensors, rewards)\n",
        "\n",
        "#         for cs in ctrl_str:\n",
        "#             key = \"env/reward_\" + cs.strip(\"[]\")\n",
        "#             stats[key] = np.mean([r.cpu().numpy() for r, t in zip(rewards, task_list) if t == cs])\n",
        "#         ppo_trainer.log_stats(stats, game_data, rewards)"
      ],
      "metadata": {
        "id": "WTiDklXBlT52"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "###saving the model\n",
        "starcoder_model.save_pretrained(\"StarCoderTinyrhlfmodel/\")\n",
        "starcoder_tokenizer.save_pretrained(\"StarCoderTinyrhlfmodel/\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MNFD6wvSlWux",
        "outputId": "7cda4399-b238-4336-a5aa-480613ece097"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('StarCoderTinyrhlfmodel/tokenizer_config.json',\n",
              " 'StarCoderTinyrhlfmodel/special_tokens_map.json',\n",
              " 'StarCoderTinyrhlfmodel/vocab.json',\n",
              " 'StarCoderTinyrhlfmodel/merges.txt',\n",
              " 'StarCoderTinyrhlfmodel/added_tokens.json',\n",
              " 'StarCoderTinyrhlfmodel/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline, set_seed\n",
        "model_path = \"StarCoderTinyrhlfmodel/\"\n",
        "set_seed(42)\n",
        "pipe = pipeline(\"text-generation\",model=model_path, tokenizer=model_path, max_length=30, num_return_sequences=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9KWg8IWoQqN",
        "outputId": "b175814c-5206-488a-fc35-da972e3719cb"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at StarCoderTinyrhlfmodel/ were not used when initializing GPTBigCodeForCausalLM: ['v_head.summary.weight', 'v_head.summary.bias']\n",
            "- This IS expected if you are initializing GPTBigCodeForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing GPTBigCodeForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ]
    }
  ]
}